2511 results about "Biologic marker" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

The invention relates to a breast cancer plasma transporter miRNA marker and application thereof, and belongs to the technical field of biological detection. In the prior art, biomarkers for breast cancer liquid biopsy have the problems of low content, high heterogeneity, insufficient time resolution and the like, so that the detection reliability is limited. According to the invention, a specific miRNA (micro Ribonucleic Acid) marker (including one or more of let-7d-5p, miR-103a-3p and miR-22-3p) of the plasma transporter is provided. The marker can be used for preparing a breast cancer detection reagent or kit. Clinical verifications show that the miRNAs are remarkably and highly expressed in plasma migrators of breast cancer patients, the AUC value reaches 0.763-0.808, the sensitivity is 66.7%-86.7%, the specificity is 66.7%-88.3%, the miRNAs have the advantages of high signal-to-noise ratio and dynamic monitoring, and the accuracy and practicability of breast cancer detection are remarkably improved.

A mask-based diagnostic apparatus is provided for detecting a biomarker contained in exhaled breath of a test subject. An exhaled breath condensate (EBC) collector converts breath vapor received from the lungs and airways of the test subject into a fluid biosample. The EBC collector including a thermal mass, a condensate-forming surface and a fluid conductor disposed on the condensate-forming surface. A fluid transfer system receives the fluid biosample from the EBC collector. A biomarker testing unit receives the fluid biosample from the fluid transfer system and tests the fluid biosample for a target biomarker. A testing system support is provided for supporting the EBC collector, the fluid transfer system and the biomarker testing unit. The testing system support is configured and dimensioned to fit inside a face mask. A face mask is provided forming an exhaled breath vapor containment volume to hold the exhaled breath vapor in proximity to the EBC collector to enable the condensate-forming surface cooled by the thermal mass to coalesce the exhaled breath vapor into the fluid biosample.

The invention discloses a lung injury evaluation system based on a vascular endothelial cell protection effect, and the system comprises a sample processing module which is configured to execute sample preprocessing and multi-dimensional biomarker collection operation; the feature calculation module is connected to the sample processing module and is configured to perform endothelial function core index quantification and feature extraction operation; the data fusion module is connected to the feature calculation module and is configured to execute lung injury degree and endothelial protection efficiency association mapping operation; and the strategy generation module is connected to the data fusion module and is configured to execute a dynamic risk assessment and assessment strategy generation stage. The method has the following advantages and effects: systematic fusion and dynamic analysis of multi-source heterogeneous data are realized, and quantitative association mapping between biomarkers and iconography features is established, so that the comprehensiveness and predictive ability of state evaluation are remarkably improved.

The invention discloses a biomarker for evaluating litchi quality and a detection method, and relates to the technical field of biology. The marker is an SNP (Single Nucleotide Polymorphism) molecular marker, and the nucleotide sequence of the marker is as shown in SEQ ID NO.1-3. The molecular marker disclosed by the invention can be used for evaluating the quality of litchis, particularly evaluating the fructose content. Meanwhile, when the gene is applied to molecular marker assisted selection (MAS) and genome selection (GS), the genetic improvement progress of the litchi variety can be accelerated.

The present disclosure, in some aspects, provides one or more biomarkers (e.g., plasma biomarkers or circulating biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., plasma biomarkers or circulating biomarkers) described herein is regulated by DUX4. Methods (e.g., non-invasive methods) of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided. The present disclosure, in other aspects, provides one or more biomarkers (e.g., transcriptome biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., transcriptome biomarkers) described herein is regulated by DUX4. Methods of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided.

The present disclosure relates to a multi-phase method for determining a set of candidate genes. During a first phase, the method includes prompting a naïve language model with a plurality of prompts corresponding to a plurality of candidate genes to generate a set of initial scores indicative of each corresponding candidate gene's potential as a biomarker or therapeutic target. During a second phase, the method includes determining, for each candidate gene, a set of relevant documents from a curated document library. The method also includes prompting a further language model using the relevant documents to generate secondary scores. During a third phase, the method includes determining, for each candidate gene, at least one of: a decision classification, a recalibrated score, and a detailed scientific explanation. The method includes determining a final candidate set and conducting a multi-dimensional optimization analysis on each candidate gene of the final candidate set.

The invention relates to the technical field of breast cancer risk prediction, and discloses a breast cancer relative survival risk prediction system based on machine learning. The system comprises four core modules, wherein a data acquisition module is used for acquiring multi-dimensional clinical diagnosis and treatment records of a patient from distributed medical nodes and synchronously acquiring dynamic biomarker time sequence information in a tumor evolution process; the feature compensation module performs missing feature compensation on the diagnosis and treatment records in a cross-modal medical data fusion scene to generate clinical feature indexes, and accordingly, individual monitoring frequency is determined; the risk analysis module extracts patient diagnosis and treatment path information in a medical network, analyzes node risk associated data, and determines a survival risk level in combination with dynamic biomarker time sequence information; and the survival early warning module realizes dynamic early warning of the treatment blind area according to the individualized monitoring frequency and the survival risk level. The system can efficiently integrate medical data, accurately assess survival risks, and provide dynamic and individualized support for clinical diagnosis and treatment of breast cancer.

The invention discloses a lung health monitoring and pneumoconiosis risk assessment method. The method comprises the following steps: establishing a quantitative mapping model of biomarker concentration and pneumoconiosis development stage; constructing an exhalant identification sensor array, and establishing a conduction model of response signals and a pneumoconiosis development stage; collecting sample data; preprocessing the data; constructing a dual-path feature enhancement strategy to perform feature extraction, and fusing time domain features and frequency domain features based on feature dimensions determined by a conduction model to construct a more comprehensive high-dimensional feature space; constructing a multi-modal diagnosis model by taking a long-short term memory network or a Transform network as a core structure, and training the multi-modal diagnosis model by utilizing the training set and the test set to obtain a lung health assessment model; and performing online monitoring and outputting a pneumoconiosis risk assessment result. According to the method, the lung health condition of the person to be monitored can be efficiently and accurately evaluated, early warning can be carried out according to the actual risk condition, and reliable technical support can be provided for early prevention and diagnosis of pneumoconiosis and daily health management.

The invention belongs to the technical field of tumor clustering analysis, and particularly relates to a tumor clustering analysis system and method based on big data. Through deep fusion and space-time alignment processing of multi-dimensional biological data, the limitation of single-dimension or simple data integration in a traditional method is overcome, complex biological characteristics and space-time evolution laws in the tumor can be captured more comprehensively, and the tumor can be rapidly and accurately captured by constructing a low-dimensional association and independent characteristic space and combining a nonlinear dimension reduction technology. According to the method, low-dimensional feature vectors with biomarker features are effectively extracted, a high-quality data basis is provided for subsequent clustering analysis, meanwhile, based on sliding window density estimation and determination of a dynamic neighborhood radius, accurate spatial adjacency relation construction of the low-dimensional feature vectors is achieved, and the accuracy of the spatial adjacency relation is improved. And the potential clustering center of the tumor subtype is extracted through the multi-scale characteristic spectrum, so that the generated classification label can more accurately reflect the real distribution of the tumor subtype.

The invention relates to the field of medical data analysis, in particular to a patient multi-dimensional feature clustering analysis method for a mental health database. The method comprises the following steps: acquiring multi-modal data of a patient from a mental health database, and performing missing value filling and standardization processing; constructing a biomarker pathway mapping knowledge domain, mapping the genetic information of the patient and biomarker data to nodes of the mapping knowledge domain, and extracting pathway activation features; fusing the dimensionality-reduced iconography and clinical features with the pathway features by adopting a multi-modal embedding method to obtain a comprehensive feature vector of the patient; grouping the patients by using a metric learning clustering algorithm and combining with pathway consistency constraints to obtain subtypes with biological significance; and further analyzing the characteristic contribution degree and the molecular mechanism difference of each subtype by adopting an interpretability method. According to the method, multi-modal data can be effectively integrated, the clustering precision and interpretability are improved, and a basis is provided for subtype recognition and personalized intervention of mental health diseases.

The invention relates to application of tsRNA-3025a as a prognostic marker of acute myocardial infarction and a treatment target spot of myocardial ischemia reperfusion injury. A DNA (Deoxyribonucleic Acid) sequence corresponding to the tsRNA-3025a is shown as SEQ ID NO: 1: 5 '-ATCCTGCCGACTACGCCA-3'. The tsRNA-3025a can be used for treating acute myocardial infarction and myocardial ischemia reperfusion injury. In the aspect of prognosis, a detection kit is provided, and the risk of heart failure and short-term adverse events of a patient is evaluated by quantitatively detecting the expression level of the tsRNA. In the aspect of treatment, the invention provides the application of the anti-tagomir for inhibiting the function or expression of tsRNA-3025a in the preparation of the medicine for treating the myocardial ischemia reperfusion injury, and the anti-tagomir is subjected to specific chemical modification. A novel biomarker is provided for prognosis risk stratification of acute myocardial infarction, and an effective treatment strategy is provided for prevention and treatment of myocardial ischemia-reperfusion injury.

This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. This invention involves collecting tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals, performing metagenomic sequencing, and statistically analyzing the sequencing data using bioinformatics to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, a combination of flora biomarkers is obtained. A binary classification prediction model constructed using this combination can maximally detect hypertension in obese children.

The invention relates to the field of biomedical treatment, and particularly discloses application of CD146 in diagnosis and treatment of rhabdomyosarcoma, and the CD146 is specifically and highly expressed in tumor tissues of the rhabdomyosarcoma. The invention provides application of a biomarker for diagnosing rhabdomyosarcoma or / and a detection reagent thereof in preparation of a product for diagnosing rhabdomyosarcoma. Meanwhile, experiments prove that the CD146-targeted chimeric antigen receptor T cell has a relatively strong tumor cell killing effect and can effectively remove CD146 positive tumor cells. The CD146 biomarker provided by the invention provides a new target and theoretical basis for early diagnosis and individualized treatment of rhabdomyosarcoma, and has important clinical application value.

Provided herein is the use of certain biomarkers, such as tryptophan and metabolites thereof for use in certain treatment methods. Suitable tryptophan metabolites that may serve as useful biomarkers include, for example, kynurenine (Kyn), kynurenic acid (KynA), and quinolinic acid. Such biomarkers may be used in identifying a subject who may benefit from treatment, predicting the responsiveness or monitoring the response of the subject to the treatment, or determining or adjusting the dosing or dosing regimen of the treatment to the subject.

The invention belongs to the field of biological microfluidics, and particularly relates to a detection device for biomarkers in blood. The invention provides a device for detecting biomarkers in blood. The device comprises a blood sampling layer, a detection layer and a micro-fluidic chip layer which are adjacent in sequence. According to the device for detecting the biomarkers in the blood, a blood sampling module, an anticoagulation module, a sample transmission module, a chemical reaction module and a signal detection module are integrated on a miniature wearable platform, and portable, painless, rapid and accurate detection is achieved.

The invention discloses a mental disease classification method fusing prior knowledge and a multi-mode self-attention mechanism, and belongs to the technical field of medical image processing. In order to solve the problems that in the current mental disease classification process, biomarker information is dispersed, and reproducibility is insufficient, multi-mode nerve image data, field priori knowledge and the intelligent analysis ability of a large language model are fully fused, and existing mental disease biomarkers are integrated, optimized and utilized. And related medical knowledge is efficiently extracted and integrated by using a large language model, so that efficient and accurate classification of mental diseases is realized. The method provides an innovative technical path and theoretical support for precise diagnosis and mechanism research of mental disorders.

A system and method for distinguishing the type of seizures in a human patient, such as an epileptic seizure (ES), or a functional or dissociative seizure (FDS). The system and method use a diagnostic analytical platform that gets heart rate variability (HRV) analytical metrics from a ECG and uses an analytical diagnostic algorithm to determine if an ES or FDS has occurred in the patient. The diagnostic analytical platform can create a model for distinguishing that a predetermined type of seizure has occurred from the HRV analytical metrics.

The invention discloses a YOLO-Drop-based 3D printing droplet microfluidic immunoassay method, which organically integrates a high-precision microfluidic chip preparation technology, a droplet-level immunoreaction system and a fluorescence imaging acquisition and deep learning detection algorithm. And full-chain closed-loop processing from droplet generation, fluorescence signal acquisition, image recognition, result statistics to concentration quantitative analysis is realized. Through deep combination of chip physical parameters and model input features, the method not only significantly improves the accuracy and stability of droplet identification and concentration quantification, but also enhances the robustness of the algorithm under complex sample conditions through multi-scale feature fusion and a signal dual screening mechanism. Meanwhile, the optimized YOLO-Drop model supports embedded efficient reasoning, so that the whole analysis process has the characteristics of real-time performance and high throughput, and the method is suitable for rapid quantitative detection of various biomarkers.

The invention belongs to the technical field related to data processing, and provides a brain network analysis method and system based on multi-network collaborative topology analysis in order to solve the problem that an analysis result is unstable due to threshold selection subjectivity in existing Alzheimer disease brain network analysis. Extracting brain regions corresponding to the default mode network, the significance network and the execution control network, calculating correlation coefficients among different brain regions to construct a function connection matrix, and converting the function connection matrix into a distance matrix meeting complex construction requirements; constructing a Vietories-Rips complex based on the distance matrix, calculating a coherence group under each scale of the filtering sequence, and extracting topological features of a 0-dimensional Betti number and a 1-dimensional Betti number; according to the method, the Betti number is extracted, a curve that the Betti number changes along with the threshold value is drawn, the difference of the two groups in topological characteristics is compared, the specific topological biomarker related to the Alzheimer's disease is identified, and richer biomarker information is provided for early diagnosis of the Alzheimer's disease.

The invention provides a biomarker, a kit and a method for predicting the aging-related physiological age of a mouse, the biomarker comprises a plurality of CpG sites with different methylation levels, and the position information of the CpG sites is as follows: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 109982829 and the like. The method can solve the problem of high cost of detecting the physiological age of the mouse through methylation level in the prior art, and is suitable for the field of animal age detection.

The invention discloses a biomarker combination for predicting lung metastasis of breast cancer and application of the biomarker combination. The biomarker combination for predicting lung metastasis of breast cancer comprises MUC16, MAP3K13, NR4A3, FAM111A, ZNF562 and NRP1. According to the invention, a GEO database and a TCGA database are combined, a biomarker combination for predicting lung metastasis of breast cancer is obtained through analysis and screening, and potential gene characteristics closely related to development of lung metastasis of breast cancer patients are further determined by analyzing expression levels of six biomarker genes and interaction with a tumor environment. And a more effective and personalized tool is provided for prediction and treatment of breast cancer.

The invention provides a biomarker prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of an initial pathological image, and obtaining to-be-processed image data; based on a pre-trained image feature extraction model, performing image feature extraction on each image block in the to-be-processed image data to obtain an image feature corresponding to each image block; based on a pre-trained cell nucleus feature extraction module, performing cell nucleus feature extraction on each image block in the to-be-processed image data to obtain a plurality of cell nucleus features; fusing the image features corresponding to the image blocks and the multiple cell nucleus features based on a pre-trained multi-modal model to obtain fused features; and predicting the fusion features through a predetermined target baseline model to obtain a biomarker prediction result corresponding to the initial pathological image. According to the embodiment, the image features and the cell nucleus features are fused, so that the prediction performance is improved.

The invention discloses a method and system for analyzing a bacterium-drug interaction panoramic dynamic mechanism by using a causal enlightenment hypergraph neural network, and electronic equipment, and belongs to the technical field of artificial intelligence and biomedical informatics. The method comprises the following steps: constructing a disease / syndrome / disease-bacterium / microorganism / metabolite-prescription / drug / component interlocking hypergraph; analyzing a bacteria-drug interaction dynamic mechanism based on a dynamic hypergraph network algorithm; optimizing the hypergraph neural network based on the prescription compatibility rule and the traditional Chinese medicine dosage; and predicting the microbial marker based on the causal enlightenment hypergraph neural network. According to the method, a multi-entity high-order relation is captured through an interlocking hypergraph structure, a time sliding window and an attenuation mechanism are introduced to analyze dynamic evolution, the traditional Chinese medicine dosage is used as an attention priori optimization network, and causal reasoning is achieved through alternate optimization of an intervention discovery and response prediction module. In a public data set test, the performance evaluation index is obviously superior to that in the prior art, and the problem of analyzing the panoramic dynamic mechanism of the bacterium-drug interaction is effectively solved.

The invention discloses an active tuberculosis diagnostic marker, a kit and application thereof. In particular to application of a reagent for detecting a diagnostic marker in preparation of a kit for diagnosing active tuberculosis, the diagnostic marker is a biomarker TRIM22 in a blood sample, and the sequence of the TRIM22 is as shown in SEQ ID NO: 1. According to the invention, TRIM22 in a blood sample is used as a biomarker for detecting active tuberculosis for the first time. Results of ROC curve analysis show that the expression level of TRIM22 in whole blood has good sensitivity and specificity for active tuberculosis diagnosis. Therefore, the invention overcomes the defects of low detection rate, long time consumption and the like of the existing active tuberculosis diagnosis, has the characteristics of good specificity and high sensitivity, and has good clinical application value for the auxiliary diagnosis of the active tuberculosis.

The invention relates to the technical field of chronic disease health management, and discloses a chronic disease health management method based on data analysis, and the method comprises the following steps: S1, obtaining medical records, wearable device data, laboratory results, self-reporting information and environment data of a patient through a multi-source collection gateway; and S2, performing desensitization processing and feature extraction on the data under a federated learning framework, and generating a multi-modal patient portrait containing time sequence, spatial features and biomarkers. According to the chronic disease health management method and system based on data analysis, through desensitization processing under a federated learning framework, on the premise of protecting patient data privacy, medical records, wearable device data, environment data and other multi-source information are effectively integrated, a comprehensive multi-modal patient portrait is formed, and the patient experience is improved. The problem that data are mutually separated in a traditional management mode is solved, and comprehensive data support is provided for subsequent risk prediction and intervention decision making.

Described herein are methods and systems for determining gene alteration states from pathology images. Also described are methods of selecting a treatment for a medical disease, and treating a patient in need thereof, by determining gene alteration states from pathology images. The disclosed methods and systems may also be used to investigate and identify biomarkers corresponding to gene alteration statues of interest.

The invention discloses a self-driven diagnosis and treatment integrated microneedle array paper-based chip as well as a preparation method and application thereof, and belongs to the technical field of biomedicine. The microneedle array chip system comprises a function integration module which at least comprises a self-powered extraction unit, a sensing detection unit and a controlled drug delivery unit. According to the microneedle array paper-based chip, biomarkers in skin interstitial fluid are efficiently collected based on a self-powered thermoelectric chemical battery microneedle, PtPB (at) PEG (at) HA nanoparticles loaded on an immunochromatography test strip combination pad are triggered to be subjected to ring opening, a hybridization chain reaction is induced to generate DNA nano-enzyme, and TMB is catalyzed to develop color; the open-loop nanoparticles can be used for SERS cross detection of biomarkers, and can be used as a thermal initiator to activate the phase change microneedle under the irradiation of near-infrared light, so that drugs are released as required, and precise treatment is realized. Besides, objective quantitative analysis of color signals is realized by constructing a convolutional neural network model, and the system has the functions of naked eye screening, intelligent quantification and personalized treatment, and can improve the home management ability of early skin cancer.

The invention belongs to the technical field of staging identification of stress injury, and particularly relates to a staging identification method of stress injury based on an intelligent image identification technology, which comprises the following steps: S1, multi-modal data acquisition and preprocessing: acquiring a time sequence image sequence of a target wound, the time sequence image sequence is a plurality of images shot for the same wound at different time points; collecting structured risk factor data of the patient; s2, wound area accurate segmentation and local feature quantification: using a semantic segmentation network to carry out pixel-level segmentation on each wound image; based on the segmentation result, objective biomarkers of the wound are quantitatively calculated; and S3, multi-dimensional image feature extraction: spatial feature extraction: using a deep convolutional neural network. According to the invention, the crossing from static evaluation to dynamic monitoring is realized, the perspectiveness and accuracy of staging are improved, and a multi-modal information fusion decision system is constructed, so that staging judgment is more in line with clinical logic.